Malware can cause serious problems for computing systems, and the detection of malware poses a difficult challenge as malicious software constantly evolves. Data mining solutions have sometimes been used to analyze various data on a computing device in the effort to detect malware. Traditionally, data mining techniques use structured data analysis to detect patterns and anomalies that can explain various phenomena. By using data analysis to detect patterns based on known malware, these solutions may be able to use similar patterns to also detect the potential for new instances of malware. Thus, these solutions may provide better security by recognizing signs of malware that may not yet be known.
However, some data is not easily analyzed by traditional data mining methods. For example, data that is not easily labeled or that does not conform to a standard format may be difficult for traditional methods to process. In some cases, the information that may be extracted from this data is lost in the process of formally defining strict categories used to classify whether the data may indicate malware. At the same time, other methods that may be able to process this data may not be able to process more complex, structured data that could provide more detailed analyses of malware. Because of the difficulty in mining both unstructured and complex data, more advanced solutions are needed to accurately detect computer malware. Accordingly, the instant disclosure identifies and addresses a need for additional and improved systems and methods for identifying malware through data mining methods.